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10.1186/s12859-021-04484-y http://scihub22266oqcxt.onion/10.1186/s12859-021-04484-y 34837942!8626732!34837942     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34837942&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       BMC+Bioinformatics 2021 ; 22 (1): 572 Nephropedia Template TP {{tp|p=34837942|t=2021. A multitask transfer learning framework for the prediction of virus-human protein-protein interactions |pdf=|usr=}}{{34837942}} gab.com Text A multitask transfer learning framework for the prediction of virus-human protein-protein interactions JO: BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=34837942 #FOAvip BACKGROUND: Viral infections are causing significant morbidity and mortality worldwide. Understanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection and pathogenesis. This could further help in prevention and treatment of virus-related diseases. However, the task of predicting protein-protein interactions between a new virus and human cells is extremely challenging due to scarce data on virus-human interactions and fast mutation rates of most viruses. RESULTS: We developed a multitask transfer learning approach that exploits the information of around 24 million protein sequences and the interaction patterns from the human interactome to counter the problem of small training datasets. Instead of using hand-crafted protein features, we utilize statistically rich protein representations learned by a deep language modeling approach from a massive source of protein sequences. Additionally, we employ an additional objective which aims to maximize the probability of observing human protein-protein interactions. This additional task objective acts as a regularizer and also allows to incorporate domain knowledge to inform the virus-human protein-protein interaction prediction model. CONCLUSIONS: Our approach achieved competitive results on 13 benchmark datasets and the case study for the SARS-COV-2 virus receptor. Experimental results show that our proposed model works effectively for both virus-human and bacteria-human protein-protein interaction prediction tasks. We share our code for reproducibility and future research at https://git.l3s.uni-hannover.de/dong/multitask-transfer . Twit Text FOAVip A multitask transfer learning framework for the prediction of virus-human protein-protein interactions ????BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=34837942 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34837942 #FOAvip English Wikipedia <ref>{{Cite pmid|34837942}}</ref> A multitask transfer learning framework for the prediction of virus-human protein-protein interactions #MMPMID34837942 Dong TN; Brogden G; Gerold G; Khosla M BMC Bioinformatics 2021[Nov]; 22 (1): 572 PMID34837942show ga BACKGROUND: Viral infections are causing significant morbidity and mortality worldwide. Understanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection and pathogenesis. This could further help in prevention and treatment of virus-related diseases. However, the task of predicting protein-protein interactions between a new virus and human cells is extremely challenging due to scarce data on virus-human interactions and fast mutation rates of most viruses. RESULTS: We developed a multitask transfer learning approach that exploits the information of around 24 million protein sequences and the interaction patterns from the human interactome to counter the problem of small training datasets. Instead of using hand-crafted protein features, we utilize statistically rich protein representations learned by a deep language modeling approach from a massive source of protein sequences. Additionally, we employ an additional objective which aims to maximize the probability of observing human protein-protein interactions. This additional task objective acts as a regularizer and also allows to incorporate domain knowledge to inform the virus-human protein-protein interaction prediction model. CONCLUSIONS: Our approach achieved competitive results on 13 benchmark datasets and the case study for the SARS-COV-2 virus receptor. Experimental results show that our proposed model works effectively for both virus-human and bacteria-human protein-protein interaction prediction tasks. We share our code for reproducibility and future research at https://git.l3s.uni-hannover.de/dong/multitask-transfer . |*COVID-19[MESH] |*Viruses[MESH] |Algorithms[MESH] |Humans[MESH] |Machine Learning[MESH] |Reproducibility of Results[MESH]A multitask transfer learning framework for the prediction of virus-hu(epmc).pdf DeepDyve Pubget Overpricing